The Cyber Security and Critical Infrastructure Protection (CyS&CIP) Research Center is the first of its kind in Morocco, and among the two first in Africa. It is a house for interdisciplinary research in cyber security, systems security, and physical systems security.

Attacks on computer systems and networks are on rise. Corporation, governments, and industrials are witnessing an augmentation of ransomwares, a threat to their operations and disruption to the services they provide. Risks incurred by individual, small and medium corporations, as well as industrials and government are higher and higher. The cyber security domains are about the three pillars: prevention, detection, and defense (passive and active). The CyS&CIP research center contributes to the international effort to advance research in cyber security and infrastructure protection and train highly qualified resources.

The Vision and ambition of CyS&CIP

• Lead in Cyber Security and Critical Infrastructure Protection in Morocco, Africa and be among the best in the world.

• Position UM6P in in the fields at national and regional levels.

• Advance theoretical and applied research in cyber security and critical infrastructure protection.

• Train highly qualified personnel in this multidisciplinary domain.

• Offer research assistance to stakeholders and industrial partners.

• Foster collaboration between researchers from different institutions, countries, and industrial and government organizations.

• Engage international community to not leave Africa behind in these critical research areas.

The Mission of CyS&CIP:

• Advance theoretical and applied research in cyber security and critical infrastructure protection.

• Explore Interdisciplinary research in cyber security and critical infrastructure protection.

• Develop and promote excellence in the selected areas of research.

• Establish strong collaboration with Academia, Industry, Government, and Corporations.

• Encourage internal and external collaboration.

• Create new synergies among researchers.

• Attract and retain outstanding faculty and graduate students.

• Offer PhD degree and master’s degree programs in interdisciplinary domains.

• Train students in cutting edge technologies.

CyS&CIP is opening a PhD position on AI- and Game Theory-based Security modeling for Cloud/IOT Systems

Supervisors: Ismail Berrada, Ahmed Ratnani (UM6P), Jamal Bentahar (Concordia University)

Cloud/IoT systems are open distributed systems that enjoy a certain level of autonomy. This topic focuses on modeling security aspects and properties of these systems. Different types of attacks can be modeled and formulated using game theory, for instance security games, Stackelberg games, partially observable stochastic games, and mean field games. We will consider the scenario of multiple attackers and multiple defenders where cooperative and competitive strategies are to be modeled. We will consider the case where defenders can learn from the attackers to develop better strategies. Uncertainty modeling and scalability are among the key aspects to be considered in the context of cloud/IoT systems. Combining AI techniques, in particular deep, reinforcement, federated and transfer learning, with game theoretical algorithms will be investigated.

References

• [1] Christopher Kiekintveld, Towhidul Islam, and Vladik Kreinovich:  “Security games with interval uncertainty”. AAMAS 2013, 231–238.

• [2] Sixie Yu, Yevgeniy Vorobeychik: “Removing Malicious Nodes from Networks”. AAMAS 2019: 314-322

• [3] Bo Yang, Min Liu:“Attack-Resilient Connectivity Game for UAV Networks using Generative Adversarial Learning”. AAMAS 2019: 1743-1751 

• [4] FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks, 2020,  arxiv

• [5] Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach. A generic framework for privacy preserving deep learning, 2018

• [6] Boudagdigue, C., Benslimane, A., Kobbane, A., Liu, J “Trust Management in Industrial Internet of Things” IEEE Transactions on Information Forensics and Security, 2020, 15, pp. 3667–3682, 9099265

Requirements

• Master or engineer in computer science or similar discipline

• Background in machine learning and optimization

• Ability to work in interdisciplinary teams and good communication skills in English

• Vey good experience in python (Pytorch) or matlab